Xu, Zeyuan (University of Pavia), Shi, Shaolong (University of Electronic Science and Technology of China), Li, Zixuan (Harbin Engineering University), Li, Duo (Newcastle University), Wang, Sheng (Newcastle University), Ferrara, Antonella (University of Pavia)

Robust Distributed Model Predictive Control of Heterogeneous Vehicle Platoons with Unidirectional Topologies under External Disturbances

Scheduled for presentation during the Invited Session "S24c-Traffic Control and Connected Autonomous Vehicles: benefits for efficiency, safety and beyond" (TH-LA-T24), Thursday, November 20, 2025, 16:40−17:00, Coolangata 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

This paper presents a robust distributed model predictive control (DMPC) framework for heterogeneous vehicle platoons operating under unidirectional communication topologies and external disturbances. Each follower vehicle solves a local optimal control problem using only information from its neighbors, without requiring prior knowledge of the global expected set point. The cost function incorporates both trajectory tracking errors and disturbance attenuation via an $H_{infty}$-based robustness term. To ensure system stability under disturbances, a disturbance-aware constraint is introduced, driving the predicted state toward the robust average of neighboring vehicles. A sufficient condition for asymptotic stability is derived by employing a total robust cost function as a Lyapunov candidate. Numerical simulations involving realistic vehicle models validate the robustness and scalability of the proposed robust DMPC approach under unidirectional platoon topologies and external disturbances.